计算机应用研究2018，Vol.35Issue(2)：381-385,5.DOI:10.3969/j.issn.1001-3695.2018.02.014
基于SBWS_GPR预测模型的不确定性多数据流异常检测方法
Outlier detection of uncertainty multiple data stream based on SBWS_GPR prediction model
摘要
Abstract
The uncertainty of collecting data stream in practical system brings a serious challenge for oudier detection and correction.Based on the characteristic of sliding basic windows sampling (SBWS) and Gaussian process regression (GPR),this paper proposed the outlier detection method of uncertainty multiple data stream based on SBWS_GPR prediction model.By collecting historical data set based on time series and introducing index number,cluster and analysis historical data set and got the mapping relation between the data set and index number.The real-time input data stream obtained was to realize outlier detection and correction by the sliding windqw pattern.And then based on the correlation between the input and output data and the GPR,set up prediction model and compared the real-time output data stream data with the prediction output data stream,to realize outlier detection and correction from two different input and output channels.关键词
不确定性/数据流/高斯过程回归/索引号/滑动窗口Key words
uncertainty/data stream/GPR/index number/sliding window分类
信息技术与安全科学引用本文复制引用
朱树才,秦宁宁..基于SBWS_GPR预测模型的不确定性多数据流异常检测方法[J].计算机应用研究,2018,35(2):381-385,5.基金项目
国家自然科学基金资助项目(61702228) （61702228）
江苏省自然科学基金资助项目(BK20170198) （BK20170198）
江苏省博士后科研项目(1601012A) （1601012A）
江苏省“六大人才高峰”计划资助项目(DZXX-026) （DZXX-026）
